In radiation therapy planning, creating a patient specific treatment plan can be a time consuming and tedious task. Many of the steps are redundant and vary little from patient to patient or plan to plan. Many of these steps can be automated using macro languages or scripts, but certain aspects are difficult without tools for writing logical expressions, loops, and other common programming functionality.
One area that is difficult to automate in current treatment planning is intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) optimization. Optimization is an iterative process where a user attempts to specify planning goals in the form of dose or biological objectives to create an ideal dose to target structures, typically a uniform dose, and minimize the dose to critical structures.
For a plan with many target and critical structures, the optimization problem has a large number of dimensions that is difficult for a user to navigate. Further, current user interfaces can contain long lists of goals for a user to control. However, only a small subset are necessary, and many can be hidden or grouped together into common goals. Even more, while it is relatively easy to create a plan that meets the goals, it is typically difficult to create an optimal plan. Plans can typically be further optimized, usually significantly, but an optimal plan is hard to define. Therefore it is hard to judge the degree of optimization in the current trial. Either additional structures can be considered or dose to existing critical structures can be further reduced. Hence, optimization can be tedious, inconsistent, non-optimal, and non-intuitive.
Intensity-modulated radiation therapy (IMRT) plans and Volumetrically Modulated Arc Therapy (VMAT) plans are typically based on a pre-treatment computed tomography (CT) scan that provides a snapshot of the patient's anatomy. However, inter-fractional patient variations may occur because of anatomical modifications. Therefore, the accuracy of IMRT/VMAT delivery may be compromised during the treatment course, potentially affecting the therapeutic index and normal tissue/organ sparing. Hence, adapting the plan for the changing anatomy may be useful in delivering high quality treatment to cancer patients.
Several tools for creating adaptive and/or composite plans are available in commercial treatment planning systems. Similarly various systems for automatic planning have been proposed and some have recently been commercialized. These tools can combine the dose from multiple plans and prescriptions using rigid and deformable transforms creating an accumulated dose. Some of these tools can even optimize a plan using an “accumulated” dose using standard IMRT optimization. However, none of these systems use an objective tuning or auto-planning system to re-optimize based on accumulated dose.
Optimization based on accumulated dose has inherent problems in standard optimization solved by auto-planning. The optimization problem has a large number of dimensions that is difficult for a user to navigatem, in particular for a plan with many target and critical structures. Further, current user interfaces can contain long lists of goals for a user to control. However, only a small subset is necessary, and many can be hidden or grouped together into common goals. While it is relatively easy to create a plan that meets the goals, it is difficult to create an optimal plan. Plans can be further optimized, usually significantly, but an optimal plan is hard to define. Therefore, it is hard to judge the degree of optimization in the current trial. Either additional structures can be considered or dose to existing critical structures can be further reduced. Hence, optimization can be tedious, inconsistent, non-optimal, and non-intuitive.